AWS EC2 GPU instances vs Aquanode
Hyperscaler GPU instances (P5, P4d) inside a full enterprise cloud
If you already run on AWS, the GPU instances are next to your VPC, your IAM and your data, and that integration is worth real money. What you pay for it is the per-GPU rate and the fact that an EBS-backed environment is an AWS environment. It does not restore anywhere else.
Where AWS EC2 GPU instances wins
- Everything else in the account: VPC, IAM, S3 adjacency, compliance posture, committed-use and Savings Plan discounts, and a procurement path enterprises already have.
- EFA networking and cluster placement for large distributed training.
- Capacity reservations that actually hold capacity, which no marketplace can promise.
Where Aquanode wins
- Your setup outlives the GPU you rented it on. Save the environment (custom nodes, model weights, packages, the config you spent an evening getting right) and bring it back up later on a completely different provider, instead of reinstalling it from scratch every session.
- Supply moved, the price moved, or the region ran dry. Your environment follows you out. It is not stranded in the account that happened to create it, which is the part that makes leaving any single vendor cheap.
- Pause and resume in place on every provider we support: it's a snapshot-and-terminate, then a fresh box restored from that snapshot, the same mechanism everywhere. (Voltage Park's adapter supports it too, it just has no live GPUs to rent right now.) On any provider, the same environment also comes back by restoring your saved setup onto a fresh box, a little slower, same result.
- Turn on automatic snapshots yourself and pick the interval (as often as every 15 minutes, 30 by default) instead of remembering to snapshot by hand or wiring up your own cron job.
- One account, one bill and one set of keys across every provider we support, rather than a separate login and invoice per vendor every time you chase capacity.
- Run a saved version of your setup as a job. If a provider takes the box back mid-run, we detect the loss and queue the run to retry on a different provider, excluding the one that just lost it, and if the job checkpoints the next attempt picks up from the last one. Jobs scale to zero when the queue empties, so an idle one is not sitting on a rented GPU, and every job is bounded in the unit we bill: its time limit times its attempts times its machines is the most a single run can cost.
AWS EC2 GPU instances pricing vs Aquanode
AWS EC2 GPU instances figures are EC2 Capacity Blocks for ML: RESERVED effective hourly rate, US regions. This is NOT the on-demand rate, which is higher; we could not read AWS's on-demand GPU rate from a first-party static page, so we quote only what AWS publishes here., read from their own pricing page on 2026-08-05. The Aquanode column is the lowest live per-GPU rate in our marketplace feed and moves on its own. The two columns are not measured the same way, so treat this as a starting point, not a quote.
Source: https://aws.amazon.com/ec2/capacityblocks/pricing/, EC2 Capacity Blocks for ML: RESERVED effective hourly rate, US regions. This is NOT the on-demand rate, which is higher; we could not read AWS's on-demand GPU rate from a first-party static page, so we quote only what AWS publishes here.. Verified 2026-08-05. Vendors change prices; check theirs before deciding.
What we do not claim
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
`aq deploy --snapshot <id>` rents the cheapest matching GPU and restores your snapshot onto it, including onto a different provider than the one it came from. Creating the snapshot is a separate step today: the standalone ogre CLI on the box writes it.
AWS EC2 GPU instances vs Aquanode: common questions
Are these AWS on-demand prices?
No, and the distinction matters. These are Capacity Blocks for ML reserved rates published by AWS, which are lower than on-demand. AWS's on-demand GPU pricing is served from an interactive calculator rather than a static page, so we do not quote it here rather than sourcing it from a third party.
Should I leave AWS for GPU work?
Not if your data, network and compliance boundary live there: the integration usually outweighs the rate. The case for moving is a workload whose expensive part is the environment, run on boxes that do not need to sit inside your VPC.
Do you pay for an AWS Capacity Block up front?
Yes. AWS states that EC2 Capacity Blocks pricing consists of a reservation fee and an operating system fee, and that the reservation fee is charged up front at the time you schedule the reservation. AWS also notes that reservation prices are updated regularly based on trends in supply and demand.
Source: https://aws.amazon.com/ec2/capacityblocks/pricing/, read 2026-09-02.
